On Sensing Principles Using Temporally Extended Bar Codes
Journal article, 2020

The detection of ionic variation patterns could be a significant marker for the diagnosis of neurological and other diseases. This paper introduces a novel idea for training chemical sensors to recognise patterns of ionic variations. By using an external voltage signal, a sensor can be trained to output distinct time-series signals depending on the state of the ionic solution. Those sequences can be analysed by a relatively simple readout layer for diagnostic purposes. The idea is demonstrated on a chemical sensor that is sensitive to zinc ions with a simple goal of classifying zinc ionic variations as either stable or varying. The study features both theoretical and experimental results. By extensive numerical simulations, it has been shown that the proposed method works successfully in silico. Distinct time-series signals are found which occur with a high probability under only one class of ionic variations. The related experimental results point in the right direction.

bar codes

Internet of Things


data compression

pattern recognition

ionic variations


Vasileios Athanasiou

Chalmers, Microtechnology and Nanoscience (MC2), Electronics Material and Systems

Kiran Tadi

The Hebrew University Of Jerusalem

Mattan Hurevich

The Hebrew University Of Jerusalem

Shlomo Yitzchaik

The Hebrew University Of Jerusalem

Aldo Jesorka

Chalmers, Chemistry and Chemical Engineering, Chemistry and Biochemistry

Zoran Konkoli

Chalmers, Microtechnology and Nanoscience (MC2), Electronics Material and Systems

IEEE Sensors Journal

1530-437X (ISSN) 15581748 (eISSN)

Vol. 20 13 6782-6791 9019829

Reservoir Computing with Real-time Data for future IT (RECORD-IT)

European Commission (EC) (EC/H2020/664786), 2015-09-01 -- 2018-08-31.

Subject Categories

Materials Chemistry

Information Science

Electrical Engineering, Electronic Engineering, Information Engineering

Areas of Advance

Materials Science



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